CN117272014A - Parameter processing method, device, equipment and storage medium - Google Patents
Parameter processing method, device, equipment and storage medium Download PDFInfo
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Abstract
The present disclosure provides a parameter processing method, apparatus, device, and storage medium. The method comprises the following steps: determining T full-winding rates of the wire ingots wound by the winding machine; the T full-coil rate in the T full-coil rates is obtained based on N first characteristic parameters acquired at the T moment, and the first characteristic parameters in the N first characteristic parameters are characteristic parameters capable of influencing the full-coil rate of a wire ingot wound by a winding machine; t is a positive integer greater than or equal to 1; t is a positive integer greater than or equal to 1 and less than or equal to T; under the condition that all M full roll rates in the T full roll rates are determined to not meet the preset full roll rate requirement, determining candidate characteristic parameter sets; the candidate characteristic parameters contained in the candidate characteristic parameter set are characteristic parameters which are selected from N first characteristic parameters corresponding to the full-volume rate and do not meet the requirement of the preset full-volume rate, and the full-volume rate does not meet the requirement of the preset full-volume rate; m is a positive integer of 1 or more and T or less.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a parameter processing method, apparatus, device, and storage medium.
Background
In the daily production process of the silk spindle, along with the completion of the winding of the silk spindle, a worker starts to carry out spot check on the full-winding rate of the silk spindle, and if the spot check result cannot reach the expected target, the worker is required to analyze parameters which possibly cause the full-winding rate to be up to the standard by experience and adjust the parameters. However, the method has strong dependence on manual experience, and parameters which enable the full-coil rate not to reach the standard cannot be quickly determined in the production process, and adjustment is timely performed, so that the production efficiency and the production quality are affected.
Disclosure of Invention
The present disclosure provides a parameter processing method, apparatus, device, and storage medium to solve or alleviate one or more technical problems in the prior art.
In a first aspect, the present disclosure provides a parameter processing method, including:
determining T full-winding rates of the wire ingots wound by the winding machine; the T full-coil rate in the T full-coil rates is obtained based on N first characteristic parameters acquired at the T moment, and the first characteristic parameters in the N first characteristic parameters are characteristic parameters capable of influencing the full-coil rate of the wire ingots coiled by the coiling machine; t is a positive integer greater than or equal to 1; t is a positive integer greater than or equal to 1 and less than or equal to T; n is a positive integer greater than or equal to 1;
Under the condition that all M full roll rates in the T full roll rates are determined to not meet the preset full roll rate requirement, determining a candidate characteristic parameter set; the candidate characteristic parameters contained in the candidate characteristic parameter set are characteristic parameters which are selected from N first characteristic parameters corresponding to the full-volume rate and do not meet the requirement of the preset full-volume rate, and the full-volume rate does not meet the requirement of the preset full-volume rate; and M is a positive integer greater than or equal to 1 and less than or equal to T.
In a second aspect, the present disclosure provides a parameter processing apparatus, including:
a result prediction unit for determining T full-winding rates of the wire rods wound by the winding machine; the T full-coil rate in the T full-coil rates is obtained based on N first characteristic parameters acquired at the T moment, and the first characteristic parameters in the N first characteristic parameters are characteristic parameters capable of influencing the full-coil rate of the wire ingots coiled by the coiling machine; t is a positive integer greater than or equal to 1; t is a positive integer greater than or equal to 1 and less than or equal to T; n is a positive integer greater than or equal to 1;
the parameter determining unit is used for determining candidate characteristic parameter sets under the condition that all M full roll rates in the T full roll rates do not meet the preset full roll rate requirement; the candidate characteristic parameters contained in the candidate characteristic parameter set are characteristic parameters which are selected from N first characteristic parameters corresponding to the full-volume rate and do not meet the requirement of the preset full-volume rate, and the full-volume rate does not meet the requirement of the preset full-volume rate; and M is a positive integer greater than or equal to 1 and less than or equal to T.
In a third aspect, an electronic device is provided, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of the embodiments of the present disclosure.
In a fourth aspect, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are for causing the computer to perform a method according to any one of the embodiments of the present disclosure.
In a fifth aspect, a computer program product is provided, comprising a computer program which, when executed by a processor, implements a method according to any of the embodiments of the present disclosure.
Like this, this disclosed scheme can be in the T that the production in-process was confirmed full volume rate to confirm the condition of reaching the standard of each full volume rate in the T full volume rate, and then under the condition that the condition of reaching the standard did not meet the requirements, in time confirm the candidate characteristic parameter that leads to full volume rate not reaching the standard, so, in time and high-efficient the condition that full volume rate of wire spindle probably did not reach the standard in the wire spindle production, simultaneously, can also monitor the parameter of coiler in real time in the production, provide support for follow-up can in time carry out automated adjustment to the coiler.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
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In the drawings, the same reference numerals refer to the same or similar parts or elements throughout the several views unless otherwise specified. The figures are not necessarily drawn to scale. It is appreciated that these drawings depict only some embodiments provided according to the disclosure and are not to be considered limiting of its scope.
FIG. 1 is a schematic flow chart diagram of a parameter processing method according to an embodiment of the present application;
FIG. 2 is a schematic flow chart diagram II of a parameter processing method according to an embodiment of the present application;
FIG. 3 is a schematic flow chart diagram III of a parameter processing method according to an embodiment of the present application;
FIG. 4 is a schematic flow chart diagram of a parameter processing method according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a parameter processing apparatus according to an embodiment of the present application;
fig. 6 is a block diagram of an electronic device for implementing a parameter processing method of an embodiment of the present disclosure.
Detailed Description
The present disclosure will be described in further detail below with reference to the accompanying drawings. In the drawings, like reference numbers indicate identical or functionally similar elements. Although various aspects of the embodiments are illustrated in the accompanying drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
In addition, numerous specific details are set forth in the following detailed description in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements, circuits, etc. well known to those skilled in the art have not been described in detail in order not to obscure the present disclosure.
During the daily production of wire rods, the winding machine used to wind the wire rods may be affected by various factors such as winding speed, speed of the first heated roll, oil content, network pressure, wind temperature, wind pressure, etc., resulting in a full rate of wound wire rods that does not meet the desired objectives. In the existing process, after the winding of the wire ingots is completed, the full-winding rate of the wire ingots is subjected to spot check by a worker, and if the spot check result cannot reach the expected target, the worker is required to analyze parameters which possibly cause the full-winding rate to be not up to standard by experience and adjust the parameters. However, the method has strong dependence on manual experience, and parameters which enable the full-coil rate not to reach the standard cannot be quickly determined in the production process, and adjustment is timely performed, so that the production efficiency and the production quality are affected. Therefore, there is an urgent need for a method that can quickly and timely determine parameters that are not acceptable for full coil rate during the production of wire ingots, thus improving the quality of production.
Based on this, the present disclosure proposes a parameter processing method to solve the above-mentioned problems.
Specifically, fig. 1 is a schematic flowchart of a parameter processing method according to an embodiment of the present application. The method is optionally applied to electronic equipment, such as personal computers, servers, server clusters and the like.
Further, the method includes at least some of the following. As shown in fig. 1, includes:
step S101: t full lap rates of the wire rods wound by the winder are determined.
Here, the T-th full-volume rate of the T full-volume rates is obtained based on the N first feature parameters acquired at the T-th time (for example, at the T-th time in the generation process). T is a positive integer greater than or equal to 1; t is a positive integer of 1 or more and T or less.
It can be understood that the t full roll rate is specifically the full roll rate corresponding to the t moment in the production process of the winding machine.
Further, a first characteristic parameter of the N first characteristic parameters is a characteristic parameter capable of affecting the full-winding rate of the wire rod wound by the winding machine; and N is a positive integer greater than or equal to 1.
It should be noted that the N first characteristic parameters include, but are not limited to, at least one of the following: the spinning process parameters such as the concentration of the oiling agent, the rotating speed of an oiling agent pump, the oil content, the wind temperature, wind pressure, the air pressure, the diameter of a spinneret plate, the winding speed, the speed of a first hot roller (GR 1), the temperature of a second hot roller (GR 2), the pressure of a network (comprising a pre-network and a main network), the overfeeding rate of a head and the like.
Step S102: and under the condition that all M full-volume rates in the T full-volume rates are determined to not meet the preset full-volume rate requirement, determining a candidate characteristic parameter set.
Here, the candidate feature parameters included in the candidate feature parameter set are feature parameters which are selected from N first feature parameters corresponding to the full-volume rate and do not meet the requirement of the preset full-volume rate, and cause (or make) the full-volume rate not meet the requirement of the preset full-volume rate; and M is a positive integer greater than or equal to 1 and less than or equal to T.
In an example, the preset full-roll rate requirement may specifically be that the full-roll rate is greater than or equal to the preset full-roll rate, where the preset full-roll rate is a tested value, and may be set according to actual needs, for example, the preset full-roll rate is 95% or 98%, which is not limited by the scheme of the present disclosure.
Further, in a specific example, M is a positive integer greater than or equal to a preset threshold and less than or equal to T, where the preset threshold is a positive integer greater than or equal to 1 and less than or equal to T, for example, the value is an intermediate value in the [1, T ] interval, which may be set according to actual needs, and the disclosure is not limited thereto.
Further, in an example, the M full volume rates of the T full volume rates may refer to: the full roll rate at the continuous M moments, or the full roll rate corresponding to the discontinuous M moments; the present disclosure is not limited in this regard.
In a specific example, when it is determined that all of the M full volume rates in the T full volume rates meet the preset full volume rate requirement, the current flow is ended. Further, after the process is finished, T full roll rates can be determined again after the specified time is reached or the specified instruction is received, and the full roll rates are processed again based on the method disclosed by the scheme, so that the full roll rates are monitored, parameters which enable the full roll rates to not reach standards are determined quickly and timely, and a foundation is laid for improving production quality.
Like this, this disclosed scheme can be in the T that the production in-process determined full volume rate to confirm the condition of reaching the standard of each full volume rate in the T full volume rate, and then under the condition that the condition of reaching the standard did not meet the requirements, in time confirm the candidate characteristic parameter that leads to full volume rate not reaching the standard, so, in time and high-efficient the condition that the full volume rate of determining the wire spindle probably not reaches the standard in wire spindle production in-process, for follow-up can in time carry out automated adjustment to the winder and provide support.
Further, compared with a scheme of analyzing the unqualified reasons through manual experience after the fact, the method does not need to rely on manual experience, and meanwhile, parameters of the winding machine can be monitored and even adjusted in real time in the production process, so that the parameter monitoring or adjusting process of the winding machine is prepositioned, and is automatic and intelligent, a large amount of labor cost and time cost are saved, and further production efficiency of the wire ingots is further improved while production quality is improved.
Fig. 2 is a schematic flow chart diagram II of a parameter processing method according to an embodiment of the present application. The method is optionally applied to electronic equipment, such as personal computers, servers, server clusters and the like. It will be appreciated that the relevant content of the method shown in fig. 1 above may also be applied to this example, and this example will not be repeated for the relevant content.
Further, the method includes at least some of the following. As shown in fig. 2, includes:
step S201: in response to the first detection instruction, a wrap prediction step is performed.
In an example, the first detection instruction may be generated upon receipt of a task instruction for wire ingot production. For example, in the process of generating the wire ingot, a first detection instruction is generated.
Step S202: and under the condition that a preset time interval is reached, executing the next winding prediction step until T times are executed, so as to obtain the full winding rate corresponding to the winding machine at T different moments.
Here, the t-th winding prediction step specifically includes:
step S202-1: and N first characteristic parameters of the winding machine at the t-th moment are obtained.
Step S202-2: and estimating and obtaining the t full-coiling rate of the wire ingot coiled by the coiling machine based on the N first characteristic parameters at the t moment.
Here, the first characteristic parameter of the N first characteristic parameters is a characteristic parameter capable of affecting a full-winding rate of the wire rod wound by the winding machine; t is a positive integer greater than or equal to 1; t is a positive integer greater than or equal to 1 and less than or equal to T; and N is a positive integer greater than or equal to 1.
Further, in a specific example, the t-th full rate of the wire ingot wound by the winder can be obtained as follows; specifically, the above estimation of the t full-winding rate of the wire rod wound by the winding machine based on the N first characteristic parameters at the t time (i.e. the step S202-2 described above) specifically includes:
and inputting the N first characteristic parameters at the t moment into a target winding prediction model, and at least obtaining a first prediction result which is output by a first branch of the target winding prediction model and corresponds to the t moment.
Here, a first branch of the target winding prediction model is used to predict a full-winding rate of a wire ingot wound by the winder; the first prediction result corresponding to the t moment comprises a first value, wherein the first value represents the predicted full-coil rate of the wire ingot coiled by the coiling machine; therefore, the method for predicting the full-coil rate efficiently can rapidly obtain the full-coil rates corresponding to different moments, improves the processing efficiency, and provides support for the follow-up judgment of whether the parameters of the winding machine need to be adjusted.
Further, in a specific example, when the first prediction result corresponding to the t time point output by the first branch of the target winding prediction model is obtained in the t-th winding prediction step, the second prediction result corresponding to the t time point output by the second branch of the target winding prediction model is also obtained.
Here, the second branch of the target winding prediction model is used for predicting the influence degree of each first characteristic parameter in the N first characteristic parameters at the t-th moment to the full winding rate; the second prediction result corresponding to the t moment comprises N second values, and an nth element in the N second values represents the influence degree of the nth first characteristic parameter in the N first characteristic parameters at the t moment.
That is, after inputting the N first feature parameters at the t-th moment into the target winding prediction model, two partial results are output, and the first partial result (i.e., the first prediction result) represents the full-winding rate corresponding to the t-th moment; the second partial result (i.e. the second prediction result) characterizes the influence degree of each first characteristic parameter input at the t-th moment on the full-roll rate.
For example, with 5 first characteristic parameters { x } at time 1 1 ,x 2 ,x 3 ,x 4 ,x 5 For example, 5 first feature parameters are input into the target winding prediction model to obtain a first prediction result corresponding to the 1 st moment output by a first branch of the target winding prediction model and a second prediction result corresponding to the 1 st moment output by a second branch of the target winding prediction model, for example, the first prediction result may be { x } 1 :a 1 ,x 2 :a 2 ,x 3 :a 3 ,x 4 :a 4 ,x 5 :a 5 And }, wherein a 1 ,a 2 ,a 3 ,a 4 ,a 5 And (3) representing a second value, specifically, representing the influence degree of the corresponding first characteristic parameter at the 1 st time on the full-roll rate.
It will be appreciated that { x in this example 1 :a 1 ,x 2 :a 2 ,x 3 :a 3 ,x 4 :a 4 ,x 5 :a 5 However, that the present disclosure is not limited to this example, and other expressions are possible in practical use.
In addition, the target winding prediction model may be a neural network model, or may be another model with interpretable capability, which is not limited by the present disclosure.
Step S203: judging whether all the M full roll rates in the T full roll rates do not meet the preset full roll rate requirement, if so, entering step S204; otherwise, the process advances to step S205.
Here, regarding the content of the M full volume rates, reference may be made to the above examples, and the description thereof will not be repeated here.
Step S204: and under the condition that all M full-volume rates in the T full-volume rates are determined to not meet the preset full-volume rate requirement, determining a candidate characteristic parameter set.
The candidate feature parameters contained in the candidate feature parameter set are feature parameters which are selected from N first feature parameters corresponding to the full-volume rate and do not meet the preset full-volume rate requirement, and the full-volume rate does not meet the preset full-volume rate requirement; and M is a positive integer greater than or equal to 1 and less than or equal to T.
Step S205: ending the flow.
Therefore, the method and the device for detecting the parameters of the winding machine have the advantages that the specific examples of obtaining the T full roll rates are provided, after the T full roll rates are determined, whether candidate parameter feature sets are required to be determined is judged based on the standard reaching conditions of the full roll rates in the T full roll rates, so that the parameter detection efficiency of the winding machine is effectively improved, the parameter detection is more automatic and intelligent, and technical support is provided for subsequent automatic adjustment of the winding machine smoothly. Meanwhile, support is provided for improving the production quality and the production efficiency of the wire ingots.
Fig. 3 is a schematic flow chart diagram III of a parameter processing method according to an embodiment of the present application. The method is optionally applied to electronic equipment, such as personal computers, servers, server clusters and the like. It will be appreciated that the relevant content of the method shown in fig. 1 and 2 above may also be applied to this example, and this example will not be repeated for the relevant content.
Further, the method includes at least some of the following. As shown in fig. 3, includes:
step S301: in response to the first detection instruction, a wrap prediction step is performed.
Step S302: and under the condition that a preset time interval is reached, executing the next winding prediction step until T times are executed, so as to obtain the full winding rate corresponding to the winding machine at T different moments.
In one example, the t-th winding prediction step specifically includes:
step S302-1: and N first characteristic parameters of the winding machine at the t-th moment are obtained.
Step S302-2: and inputting the N first characteristic parameters at the t moment into a target winding prediction model to obtain a first prediction result corresponding to the t moment and output by a first branch of the target winding prediction model, and obtaining a second prediction result corresponding to the t moment and output by a second branch of the target winding prediction model.
It can be understood that after T winding prediction steps are performed, first prediction results corresponding to T different times and second prediction results corresponding to T different times can be obtained.
Here, a first branch of the target winding prediction model is used to predict a full-winding rate of a wire ingot wound by the winder; the first prediction result corresponding to the t time comprises a first value, wherein the first value represents the predicted full-coil rate of the wire ingot coiled by the coiling machine.
Further, the second branch of the target winding prediction model is used for predicting the influence degree of each first characteristic parameter in N first characteristic parameters at the t-th moment to the full winding rate; the second prediction result corresponding to the t moment comprises N second values, and an nth element in the N second values represents the influence degree of the nth first characteristic parameter in the N first characteristic parameters at the t moment.
Step S303: judging whether all the M full roll rates in the T full roll rates do not meet the preset full roll rate requirement, if so, entering step S304; otherwise, step S306 is entered.
Here, M is a positive integer greater than or equal to 1 and less than or equal to T, and for the relevant content of M full-volume rates, reference may be made to the above examples, which are not repeated here.
Step S304: and selecting second prediction results corresponding to M full roll rates which do not meet the requirement of the preset full roll rate from the T second prediction results, wherein the total of the M second prediction results. Step S305 is entered.
Here, the T second prediction results are obtained after performing T winding prediction steps.
Step S305: and obtaining the candidate characteristic parameter set based on second prediction results corresponding to M full-volume rates which do not meet the preset full-volume rate requirement.
In a specific example, the candidate feature parameter set may be obtained in the following manner (such as the first manner or the second manner); specifically, the above-mentioned second prediction result (i.e. step S305 described above) corresponding to M full-volume rates that do not meet the preset full-volume rate requirement specifically includes:
mode one: obtaining M groups of initial sets based on second prediction results corresponding to M full roll rates which do not meet the preset full roll rate requirements; candidate feature parameters are selected from the M sets of initial sets to obtain candidate feature parameter sets.
Here, the initial set of the M groups of initial sets includes: and in a second prediction result corresponding to the full-roll rate which does not meet the requirement of the preset full-roll rate, the second value is larger than the first characteristic parameter of the preset numerical value. That is, the initial set includes a main first characteristic parameter that causes the full rate to not meet the preset full rate requirement.
Further, selecting candidate feature parameters from the M sets of initial sets to obtain a candidate feature parameter set may specifically include: counting the times of all elements contained in the M groups of initial sets, and selecting a preset number of target elements with larger counted times as candidate characteristic parameters, wherein the preset number is a positive integer greater than or equal to 1 and less than N.
For example, taking the second prediction results corresponding to 3 full-volume rates that do not meet the preset full-volume rate requirement as an example, at this time, the second prediction results corresponding to the full-volume rates that do not meet the preset full-volume rate requirement may be expressed as: second prediction result-1: { x 1 :0.7,x 2 :0.4,x 3 :0.55,x 4 0.1}, second predictor-2: { x 1 :0.6,x 2 :0.45,x 3 :0.4,x 4 0.5}, and a second predictor-3: { x 1 :0.7,x 2 :0.65,x 3 :0.45,x 4 0.55, at this time, selecting a first characteristic parameter x with a second value greater than a preset value of 0.5 from the second prediction result-1 1 ,x 3 (which may be described as the initial set-1: { x) 1 ,x 3 -0.5) selecting a first characteristic parameter x having a second value greater than a predetermined value from the second prediction result-2 1 ,x 4 (which may be described as the initial set-2: { x) 1 ,x 4 -0.5) selecting from the second predictor-3 a first characteristic parameter x having a second value greater than a predetermined value 1 ,x 2 ,x 4 (can be noted as initial set-3: { x) 1 ,x 2 ,x 4 Total of 3 initial sets).
Further, counting the number of occurrences of each element contained in the 3 initial sets to obtain x 1 The statistical number of times is 3, x 4 The statistical number of times is 2, x 2 And x 3 The statistics times of the number of times are 1; at this time, the first 2 elements x with larger statistical value are selected 1 ,x 4 As candidate feature parameters.
Mode two: obtaining a characteristic parameter matrix of M rows and N columns based on second prediction results corresponding to M full roll rates which do not meet the requirement of the preset full roll rate; here, each row in the feature parameter matrix corresponds to N second values, and the second values of the same first feature parameter corresponding to different full roll rates are located in the same column; weighting columns in the characteristic parameter matrix to obtain vectors of 1 row by N columns; obtaining the candidate characteristic parameter set based on the vector of 1 row by N column; the 1 row by N column vector represents N third values; and obtaining the candidate characteristic parameter set based on the vector of 1 row and N columns.
For example, taking an M value of 3 (i.e. the full rate that does not meet the preset full rate requirement is three) and an N value of 4 (4 first feature parameters collected at each moment) as examples, at this time, the second prediction results corresponding to the full rate that does not meet the preset full rate requirement may be respectively recorded as: the second prediction result corresponding to the full volume rate-1 (may be denoted as { a } 11 ,a 12 ,a 13 ,a 14 Second prediction result corresponding to full volume rate-2 (may be denoted as { a }) 21 ,a 22 ,a 23 ,a 24 Second prediction result corresponding to full volume rate-3 (may be denoted as { a }) 31 ,a 32 ,a 33 ,a 34 -a) wherein a represents a second value, a ·1 Representing a first characteristic parameter x 1 Degree of influence on full roll Rate, a ·2 Representing a first characteristic parameter x 2 Degree of influence on full roll Rate, a ·3 Representing a first characteristic parameter x 3 Degree of influence on full roll Rate, a ·4 Representing a first characteristic parameter x 4 The influence degree on the full roll rate can be obtained at this time, and the characteristic parameter matrix of 3 rows by 4 columns is as follows:
further, weighting the second value of each column in the feature parameter matrix, e.g. to obtain
Wherein b 1 Representing full roll rate-1, b 2 Representing full roll rate-2, b 3 Indicating a full roll rate of-3.
Thus, 4 third values are obtained, which can be represented by a 1 row by 4 column vector, such as can be noted as
Further, the obtaining the candidate feature parameter set based on the 1 row×n column vector may specifically include: selecting a preset number (such as N/2) of first characteristic parameters with a larger third value from the vectors of 1 row and N columns as candidate characteristic parameters; the preset number is a positive integer greater than or equal to 1 and less than N.
Step S306: ending the flow.
In this way, the scheme of the present disclosure provides a specific example of feature prediction, so that important parameters that make the full-coil rate not reach the standard can be determined efficiently, the parameter detection efficiency of the winding machine is effectively improved, and technical support is provided for subsequent automatic adjustment of the winding machine. Meanwhile, support is provided for improving the production quality and the production efficiency of the wire ingots.
Fig. 4 is a schematic flow chart diagram of a parameter processing method according to an embodiment of the present application. The method is optionally applied to electronic equipment, such as personal computers, servers, server clusters and the like. It will be appreciated that the relevant content of the methods shown in fig. 1, 2 and 3 above may also be applied to this example, and this example will not be repeated for the relevant content.
Further, the method includes at least some of the following. As shown in fig. 4, includes:
step S401: t full lap rates of the wire rods wound by the winder are determined.
Here, the T-th full-volume rate of the T full-volume rates is obtained based on the N first characteristic parameters acquired at the T-th time; the first characteristic parameters in the N first characteristic parameters are characteristic parameters capable of influencing the full-coiling rate of the wire ingots coiled by the coiling machine; t is a positive integer greater than or equal to 1; t is a positive integer greater than or equal to 1 and less than or equal to T; and N is a positive integer greater than or equal to 1.
Here, for the relevant content of the first characteristic parameter, reference may be made to the above example, and the description thereof will not be repeated here.
Step S402: judging whether all the M full roll rates in the T full roll rates do not meet the preset full roll rate requirement, if so, entering step S403; otherwise, the process advances to step S409.
Step S403: and under the condition that all M full-volume rates in the T full-volume rates are determined to not meet the preset full-volume rate requirement, determining a candidate characteristic parameter set. And proceeds to step S404.
The candidate feature parameters contained in the candidate feature parameter set are feature parameters which are selected from N first feature parameters corresponding to the full-volume rate and do not meet the preset full-volume rate requirement, and the full-volume rate does not meet the preset full-volume rate requirement; and M is a positive integer greater than or equal to 1 and less than or equal to T. In addition, regarding the relevant content of the M full volume rates and the preset full volume rate requirements, reference may be made to the above examples, which are not repeated here.
Step S404: and performing simulation adjustment on each candidate characteristic parameter in the candidate characteristic parameter set. And proceeds to step S405.
It should be noted that the simulation adjustment means that the candidate feature parameters in the candidate feature parameter set are subjected to numerical adjustment so as to predict the full-roll rate again based on the adjusted feature parameters, so that an adjustment mode capable of improving the full-roll rate and enabling the full-roll rate to reach the preset full-roll rate requirement is conveniently determined.
Step S405: and re-estimating and obtaining a new full-coil rate of the wire rod coiled by the coiling machine at least based on each candidate characteristic parameter after simulation and adjustment. And proceeds to step S406.
For example, in an example, each candidate feature parameter after the simulation adjustment and other first feature parameters except the candidate feature parameter acquired at the time of the acquisition of the candidate feature parameter may be input to the target winding prediction model again to predict the full-winding rate.
Step S406: judging whether the new full-coil rate of the wire ingot coiled by the coiling machine meets the preset full-coil rate requirement, if so, entering step S407; otherwise, returning to step S404, to perform simulation adjustment on each candidate feature parameter in the candidate feature parameter set again.
Step S407: and obtaining a target adjustment mode under the condition that the new full-coil rate of the wire ingot coiled by the coiling machine meets the preset full-coil rate requirement. And proceeds to step S408.
Step S408: based on the target adjustment mode, a first adjustment instruction is generated.
Here, the first adjustment instruction is configured to instruct physical adjustment of each candidate feature parameter in the candidate feature parameter set of the winder in the target adjustment manner.
Step S409: ending the flow.
Like this, this disclosed scheme provides a specific example to the parameter of winder carries out automated adjustment, so, can monitor the parameter of winder in real time in the production process, even adjust for the parameter monitoring of winder or adjustment process are leading, and automatic and intelligent, moreover, have saved a large amount of human costs and time cost, and then when promoting production quality, have further promoted the production efficiency of wire spindle.
In summary, the present disclosure provides a parameter processing method, which has the following advantages over the prior art, and the method specifically includes:
first, the response is more timely. Compared with a scheme which relies on analyzing the unqualified reasons through manual experience afterwards, the method and the device can rapidly determine the characteristic parameters which lead to the unqualified rate of the full coil by predicting the full coil rate in the process of producing the wire ingots, and the response speed is faster once the full coil rate is detected to be unqualified, the scheme does not need to rely on manual experience, and simultaneously, the parameters of a winding machine can be monitored and even adjusted in real time in the production process.
Second, more automated. Compared with the post-processing technical scheme, the technical scheme can quickly determine the characteristic parameters which are not up to standard for the full-winding rate in the process of producing the wire ingots, and can timely make a parameter adjustment scheme, so that the full-winding rate of the wire ingots wound by the winding machine meets the production standard, and the wire ingot production device is more automatic and intelligent, so that a large amount of labor cost and time cost are saved.
Thirdly, the production efficiency and the production quality are improved. Compared with the prior art, the parameter monitoring or adjusting process of the winding machine in the scheme is front, automatic and intelligent, so that a large amount of labor cost and time cost are saved, and the production efficiency of the wire ingots is further improved while the production quality is improved.
The present disclosure further provides a parameter processing apparatus, as shown in fig. 5, including:
a result prediction unit 501 for determining T full-winding rates of the wire rods wound by the winding machine; the T full-coil rate in the T full-coil rates is obtained based on N first characteristic parameters acquired at the T moment, and the first characteristic parameters in the N first characteristic parameters are characteristic parameters capable of influencing the full-coil rate of the wire ingots coiled by the coiling machine; t is a positive integer greater than or equal to 1; t is a positive integer greater than or equal to 1 and less than or equal to T; n is a positive integer greater than or equal to 1;
a parameter determining unit 502, configured to determine a candidate feature parameter set when it is determined that none of the M full-volume rates in the T full-volume rates meets a preset full-volume rate requirement; the candidate characteristic parameters contained in the candidate characteristic parameter set are characteristic parameters which are selected from N first characteristic parameters corresponding to the full-volume rate and do not meet the requirement of the preset full-volume rate, and the full-volume rate does not meet the requirement of the preset full-volume rate; and M is a positive integer greater than or equal to 1 and less than or equal to T.
In a specific example of the solution of the present disclosure, the result prediction unit is specifically configured to:
in response to the first detection instruction, performing a wrap prediction step;
under the condition that a preset time interval is reached, executing the next winding prediction step until T times are executed, so as to obtain full winding rates corresponding to the winding machine at T different moments;
wherein the t-th winding prediction step includes:
acquiring N first characteristic parameters of the winding machine at the t-th moment;
and estimating and obtaining the t full-coiling rate of the wire ingot coiled by the coiling machine based on the N first characteristic parameters at the t moment.
In a specific example of the solution of the present disclosure, the result prediction unit is specifically configured to:
inputting the N first characteristic parameters at the t moment into a target winding prediction model, and at least obtaining a first prediction result which is output by a first branch of the target winding prediction model and corresponds to the t moment;
wherein a first branch of the target winding prediction model is used for predicting the full-winding rate of a wire ingot wound by the winding machine; the first prediction result corresponding to the t time comprises a first value, wherein the first value represents the predicted full-coil rate of the wire ingot coiled by the coiling machine.
In a specific example of the solution of the present disclosure, the result prediction unit is further configured to:
obtaining a second prediction result corresponding to the t moment output by a second branch of the target winding prediction model under the condition that a first prediction result corresponding to the t moment output by a first branch of the target winding prediction model is obtained in the t-th winding prediction step;
the second branch of the target winding prediction model is used for predicting the influence degree of each first characteristic parameter in N first characteristic parameters at the t-th moment on the full winding rate; the second prediction result corresponding to the t moment comprises N second values, and an nth element in the N second values represents the influence degree of the nth first characteristic parameter in the N first characteristic parameters at the t moment.
In a specific example of the solution of the present disclosure, the parameter determining unit is specifically configured to:
selecting second prediction results corresponding to M full roll rates which do not meet the requirement of the preset full roll rate from the T second prediction results; wherein the T second prediction results are obtained after T winding prediction steps are executed;
and obtaining the candidate characteristic parameter set based on second prediction results corresponding to M full-volume rates which do not meet the preset full-volume rate requirement.
In a specific example of the solution of the present disclosure, the parameter determining unit is specifically configured to:
obtaining M groups of initial sets based on second prediction results corresponding to M full roll rates which do not meet the preset full roll rate requirements; wherein, the initial set in the M groups of initial sets comprises: in a second prediction result corresponding to the full-roll rate which does not meet the requirement of the preset full-roll rate, the second value is larger than a first characteristic parameter of a preset numerical value;
candidate feature parameters are selected from the M sets of initial sets to obtain candidate feature parameter sets.
In a specific example of the solution of the present disclosure, the parameter determining unit is specifically configured to:
obtaining a characteristic parameter matrix of M rows and N columns based on second prediction results corresponding to M full-volume rates which do not meet the requirement of a preset full-volume rate, wherein each row in the characteristic parameter matrix corresponds to N second values, and the second values of the same first characteristic parameters corresponding to different full-volume rates are located in the same column;
weighting columns in the characteristic parameter matrix to obtain vectors of 1 row and N columns, wherein the vectors of 1 row and N columns represent N third values;
and obtaining the candidate characteristic parameter set based on the vector of 1 row and N columns.
In a specific example of the present disclosure, further comprising: a parameter adjustment unit; wherein,
the parameter adjustment unit is used for performing simulation adjustment on each candidate characteristic parameter in the candidate characteristic parameter set;
the result prediction unit is further used for re-estimating and obtaining a new full-coil rate of the wire ingot coiled by the coiling machine at least based on the candidate characteristic parameters after simulation and adjustment;
the parameter adjusting unit is further configured to obtain a target adjustment mode when a new full-winding rate of the wire ingot wound by the winding machine meets a preset full-winding rate requirement.
In a specific example of the solution of the present disclosure, the parameter adjustment unit is further configured to:
generating a first adjustment instruction based on the target adjustment mode; the first adjustment instruction is used for indicating to perform physical adjustment on each candidate characteristic parameter in the candidate characteristic parameter set of the winding machine according to the target adjustment mode.
In a specific example of the present disclosure, the parameter adjustment unit is further configured to, when a new full-winding rate of the wire rod wound by the winding machine does not meet a preset full-winding rate requirement, perform a simulation adjustment on each candidate feature parameter in the candidate feature parameters again;
The result prediction unit is further configured to determine a new full-roll rate after performing simulation adjustment on each candidate feature parameter in the candidate feature parameter set again, and determine whether the new full-roll rate meets a preset full-roll rate requirement.
Descriptions of specific functions and examples of each unit of the apparatus in the embodiments of the present disclosure may refer to related descriptions of corresponding steps in the foregoing method embodiments, which are not repeated herein.
Fig. 6 is a block diagram of an electronic device according to an embodiment of the present disclosure. As shown in fig. 6, the electronic device includes: a memory 610 and a processor 620, the memory 610 storing a computer program executable on the processor 620. The number of memory 610 and processors 620 may be one or more. The memory 610 may store one or more computer programs that, when executed by the electronic device, cause the electronic device to perform the methods provided by the method embodiments described above. The electronic device may further include: the communication interface 630 is used for communicating with external devices for data interactive transmission.
If the memory 610, the processor 620, and the communication interface 630 are implemented independently, the memory 610, the processor 620, and the communication interface 630 may be connected to each other and perform communication with each other through buses. The bus may be an industry standard architecture (Industry Standard Architecture, ISA) bus, an external device interconnect (Peripheral Component Interconnect, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in fig. 6, but not only one bus or one type of bus.
Alternatively, in a specific implementation, if the memory 610, the processor 620, and the communication interface 630 are integrated on a chip, the memory 610, the processor 620, and the communication interface 630 may communicate with each other through internal interfaces.
It should be appreciated that the processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processing, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or any conventional processor or the like. It is noted that the processor may be a processor supporting an advanced reduced instruction set machine (Advanced RISC Machines, ARM) architecture.
Further, optionally, the memory may include a read-only memory and a random access memory, and may further include a nonvolatile random access memory. The memory may be volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), programmable ROM (PROM), erasable Programmable ROM (EPROM), electrically Erasable EPROM (EEPROM), or flash Memory, among others. Volatile memory can include random access memory (Random Access Memory, RAM), which acts as external cache memory. By way of example, and not limitation, many forms of RAM are available. For example, static RAM (SRAM), dynamic RAM (Dynamic Random Access Memory, DRAM), synchronous DRAM (SDRAM), double Data rate Synchronous DRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous DRAM (SLDRAM), and Direct RAMBUS RAM (DR RAM).
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, the processes or functions described in accordance with the embodiments of the present disclosure are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by a wired (e.g., coaxial cable, fiber optic, data subscriber line (Digital Subscriber Line, DSL)) or wireless (e.g., infrared, bluetooth, microwave, etc.) means. The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., digital versatile Disk (Digital Versatile Disc, DVD)), or a semiconductor medium (e.g., solid State Disk (SSD)), etc. It is noted that the computer readable storage medium mentioned in the present disclosure may be a non-volatile storage medium, in other words, may be a non-transitory storage medium.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program for instructing relevant hardware, where the program may be stored in a computer readable storage medium, and the storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
In the description of embodiments of the present disclosure, a description of reference to the terms "one embodiment," "some embodiments," "examples," "particular examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present disclosure. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
In the description of the embodiments of the present disclosure, unless otherwise indicated, "/" means or, for example, a/B may represent a or B. "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone.
In the description of the embodiments of the present disclosure, the terms "first," "second," and "second" are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the embodiments of the present disclosure, unless otherwise indicated, the meaning of "a plurality" is two or more.
The foregoing description of the exemplary embodiments of the present disclosure is not intended to limit the present disclosure, but rather, any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (22)
1. A parameter processing method, comprising:
determining T full-winding rates of the wire ingots wound by the winding machine; the T full-coil rate in the T full-coil rates is obtained based on N first characteristic parameters acquired at the T moment, and the first characteristic parameters in the N first characteristic parameters are characteristic parameters capable of influencing the full-coil rate of the wire ingots coiled by the coiling machine; t is a positive integer greater than or equal to 1; t is a positive integer greater than or equal to 1 and less than or equal to T; n is a positive integer greater than or equal to 1;
Under the condition that all M full roll rates in the T full roll rates are determined to not meet the preset full roll rate requirement, determining a candidate characteristic parameter set; the candidate characteristic parameters contained in the candidate characteristic parameter set are characteristic parameters which are selected from N first characteristic parameters corresponding to the full-volume rate and do not meet the requirement of the preset full-volume rate, and the full-volume rate does not meet the requirement of the preset full-volume rate; and M is a positive integer greater than or equal to 1 and less than or equal to T.
2. The method of claim 1, wherein the determining T full coil rates of the wire ingot wound by the winder comprises:
in response to the first detection instruction, performing a wrap prediction step;
under the condition that a preset time interval is reached, executing the next winding prediction step until T times are executed, so as to obtain full winding rates corresponding to the winding machine at T different moments;
wherein the t-th winding prediction step includes:
acquiring N first characteristic parameters of the winding machine at the t-th moment;
and estimating and obtaining the t full-coiling rate of the wire ingot coiled by the coiling machine based on the N first characteristic parameters at the t moment.
3. The method according to claim 2, wherein estimating the t-th full roll rate of the wire rod wound by the winder based on the N first characteristic parameters at the t-th time point comprises:
Inputting the N first characteristic parameters at the t moment into a target winding prediction model, and at least obtaining a first prediction result which is output by a first branch of the target winding prediction model and corresponds to the t moment;
wherein a first branch of the target winding prediction model is used for predicting the full-winding rate of a wire ingot wound by the winding machine; the first prediction result corresponding to the t time comprises a first value, wherein the first value represents the predicted full-coil rate of the wire ingot coiled by the coiling machine.
4. A method according to claim 3, wherein the t-th wrap prediction step further comprises:
obtaining a second prediction result corresponding to the t moment output by a second branch of the target winding prediction model under the condition that a first prediction result corresponding to the t moment output by a first branch of the target winding prediction model is obtained in the t-th winding prediction step;
the second branch of the target winding prediction model is used for predicting the influence degree of each first characteristic parameter in N first characteristic parameters at the t-th moment on the full winding rate; the second prediction result corresponding to the t moment comprises N second values, and an nth element in the N second values represents the influence degree of the nth first characteristic parameter in the N first characteristic parameters at the t moment.
5. The method of claim 4, wherein the determining a candidate feature parameter set comprises:
selecting second prediction results corresponding to M full roll rates which do not meet the requirement of the preset full roll rate from the T second prediction results; wherein the T second prediction results are obtained after T winding prediction steps are executed;
and obtaining the candidate characteristic parameter set based on second prediction results corresponding to M full-volume rates which do not meet the preset full-volume rate requirement.
6. The method of claim 5, wherein the obtaining the candidate feature parameter set based on the second prediction results corresponding to M full-volume rates that do not meet the preset full-volume rate requirement includes:
obtaining M groups of initial sets based on second prediction results corresponding to M full roll rates which do not meet the preset full roll rate requirements; wherein, the initial set in the M groups of initial sets comprises: in a second prediction result corresponding to the full-roll rate which does not meet the requirement of the preset full-roll rate, the second value is larger than a first characteristic parameter of a preset numerical value;
candidate feature parameters are selected from the M sets of initial sets to obtain candidate feature parameter sets.
7. The method of claim 5, wherein the obtaining the candidate feature parameter set based on the second prediction results corresponding to M full-volume rates that do not meet the preset full-volume rate requirement includes:
Obtaining a characteristic parameter matrix of M rows and N columns based on second prediction results corresponding to M full-volume rates which do not meet the requirement of a preset full-volume rate, wherein each row in the characteristic parameter matrix corresponds to N second values, and the second values of the same first characteristic parameters corresponding to different full-volume rates are located in the same column;
weighting columns in the characteristic parameter matrix to obtain vectors of 1 row and N columns, wherein the vectors of 1 row and N columns represent N third values;
and obtaining the candidate characteristic parameter set based on the vector of 1 row and N columns.
8. The method of any of claims 3-7, wherein after determining the candidate feature parameter set, further comprising:
performing simulation adjustment on each candidate characteristic parameter in the candidate characteristic parameter set;
re-estimating to obtain a new full-coil rate of the wire rod coiled by the coiling machine at least based on each candidate characteristic parameter after simulation and adjustment;
and obtaining a target adjustment mode under the condition that the new full-coil rate of the wire ingot coiled by the coiling machine meets the preset full-coil rate requirement.
9. The method of claim 8, further comprising:
generating a first adjustment instruction based on the target adjustment mode; the first adjustment instruction is used for indicating to perform physical adjustment on each candidate characteristic parameter in the candidate characteristic parameter set of the winding machine according to the target adjustment mode.
10. The method of claim 8 or 9, further comprising:
and under the condition that the new full-coil rate of the wire ingot wound by the winding machine does not meet the preset full-coil rate requirement, carrying out simulation adjustment on each candidate characteristic parameter in the candidate characteristic parameter set again to determine the new full-coil rate, and judging whether the new full-coil rate meets the preset full-coil rate requirement.
11. A parameter processing apparatus comprising:
a result prediction unit for determining T full-winding rates of the wire rods wound by the winding machine; the T full-coil rate in the T full-coil rates is obtained based on N first characteristic parameters acquired at the T moment, and the first characteristic parameters in the N first characteristic parameters are characteristic parameters capable of influencing the full-coil rate of the wire ingots coiled by the coiling machine; t is a positive integer greater than or equal to 1; t is a positive integer greater than or equal to 1 and less than or equal to T; n is a positive integer greater than or equal to 1;
the parameter determining unit is used for determining candidate characteristic parameter sets under the condition that all M full roll rates in the T full roll rates do not meet the preset full roll rate requirement; the candidate characteristic parameters contained in the candidate characteristic parameter set are characteristic parameters which are selected from N first characteristic parameters corresponding to the full-volume rate and do not meet the requirement of the preset full-volume rate, and the full-volume rate does not meet the requirement of the preset full-volume rate; and M is a positive integer greater than or equal to 1 and less than or equal to T.
12. The apparatus of claim 11, wherein the result prediction unit is specifically configured to:
in response to the first detection instruction, performing a wrap prediction step;
under the condition that a preset time interval is reached, executing the next winding prediction step until T times are executed, so as to obtain full winding rates corresponding to the winding machine at T different moments;
wherein the t-th winding prediction step includes:
acquiring N first characteristic parameters of the winding machine at the t-th moment;
and estimating and obtaining the t full-coiling rate of the wire ingot coiled by the coiling machine based on the N first characteristic parameters at the t moment.
13. The apparatus of claim 12, wherein the result prediction unit is specifically configured to:
inputting the N first characteristic parameters at the t moment into a target winding prediction model, and at least obtaining a first prediction result which is output by a first branch of the target winding prediction model and corresponds to the t moment;
wherein a first branch of the target winding prediction model is used for predicting the full-winding rate of a wire ingot wound by the winding machine; the first prediction result corresponding to the t time comprises a first value, wherein the first value represents the predicted full-coil rate of the wire ingot coiled by the coiling machine.
14. The apparatus of claim 13, wherein the result prediction unit is further configured to:
obtaining a second prediction result corresponding to the t moment output by a second branch of the target winding prediction model under the condition that a first prediction result corresponding to the t moment output by a first branch of the target winding prediction model is obtained in the t-th winding prediction step;
the second branch of the target winding prediction model is used for predicting the influence degree of each first characteristic parameter in N first characteristic parameters at the t-th moment on the full winding rate; the second prediction result corresponding to the t moment comprises N second values, and an nth element in the N second values represents the influence degree of the nth first characteristic parameter in the N first characteristic parameters at the t moment.
15. The apparatus according to claim 14, wherein the parameter determination unit is specifically configured to:
selecting second prediction results corresponding to M full roll rates which do not meet the requirement of the preset full roll rate from the T second prediction results; wherein the T second prediction results are obtained after T winding prediction steps are executed;
and obtaining the candidate characteristic parameter set based on second prediction results corresponding to M full-volume rates which do not meet the preset full-volume rate requirement.
16. The apparatus according to claim 15, wherein the parameter determination unit is specifically configured to:
obtaining M groups of initial sets based on second prediction results corresponding to M full roll rates which do not meet the preset full roll rate requirements; wherein, the initial set in the M groups of initial sets comprises: in a second prediction result corresponding to the full-roll rate which does not meet the requirement of the preset full-roll rate, the second value is larger than a first characteristic parameter of a preset numerical value;
candidate feature parameters are selected from the M sets of initial sets to obtain candidate feature parameter sets.
17. The apparatus according to claim 15, wherein the parameter determination unit is specifically configured to:
obtaining a characteristic parameter matrix of M rows and N columns based on second prediction results corresponding to M full-volume rates which do not meet the requirement of a preset full-volume rate, wherein each row in the characteristic parameter matrix corresponds to N second values, and the second values of the same first characteristic parameters corresponding to different full-volume rates are located in the same column;
weighting columns in the characteristic parameter matrix to obtain vectors of 1 row and N columns, wherein the vectors of 1 row and N columns represent N third values;
and obtaining the candidate characteristic parameter set based on the vector of 1 row and N columns.
18. The apparatus of any of claims 13-17, further comprising: a parameter adjustment unit; wherein,
the parameter adjustment unit is used for performing simulation adjustment on each candidate characteristic parameter in the candidate characteristic parameter set;
the result prediction unit is further used for re-estimating and obtaining a new full-coil rate of the wire ingot coiled by the coiling machine at least based on the candidate characteristic parameters after simulation and adjustment;
the parameter adjusting unit is further configured to obtain a target adjustment mode when a new full-winding rate of the wire ingot wound by the winding machine meets a preset full-winding rate requirement.
19. The apparatus of claim 18, wherein the parameter adjustment unit is further configured to:
generating a first adjustment instruction based on the target adjustment mode; the first adjustment instruction is used for indicating to perform physical adjustment on each candidate characteristic parameter in the candidate characteristic parameter set of the winding machine according to the target adjustment mode.
20. The device according to claim 18 or 19, wherein,
the parameter adjusting unit is further configured to perform simulation adjustment on each candidate characteristic parameter in the candidate characteristic parameters again when the new full-coil rate of the wire rod coiled by the coiling machine does not meet the preset full-coil rate requirement;
The result prediction unit is further configured to determine a new full-roll rate after performing simulation adjustment on each candidate feature parameter in the candidate feature parameter set again, and determine whether the new full-roll rate meets a preset full-roll rate requirement.
21. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-10.
22. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-10.
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